Volume 104, Issue 1, July 1998
Index of content:
- ACOUSTIC SIGNAL PROCESSING 
104(1998); http://dx.doi.org/10.1121/1.423278View Description Hide Description
Most acoustic sources found in the ocean environment are spatially complex and broadband. In the case of shallow water propagation, these source characteristics complicate the analysis of received acoustic data considerably. A common approach to the broadband problem is to decompose the received signal into a set of narrow-band lines. This then allows the problem to be treated as a multiplicity of narrow-band problems. Here a model-based approach is developed for the processing of data received on a vertical array from a broadband source where it is assumed that the propagation is governed by the normal-mode model. The goal of the processor is to provide an enhanced (filtered) version of the pressure at the array and the modal functions. Thus a pre-processor is actually developed, since one could think of several applications for these enhanced quantities such as localization, modal estimation, etc. It is well-known that in normal-mode theory a different modal structure evolves for each temporal frequency; thus it is not surprising that the model-based solution to this problem results in a scheme that requires a “bank” of narrow-band model-based processors—each with its own underlying modal structure for the narrow frequency band it operates over. The “optimal” Bayesian solution to the broadband pressure field enhancement and modal function extraction problem is developed. It is shown how this broadband processor can be implemented (using a suboptimal scheme) in pseudo real time due to its inherent parallel structure. A set of noisy broadband data is synthesized to demonstrate how to construct the processor and achieve a minimum variance (optimal Bayesian) design. It is shown that both broadband pressure-field and modal function estimates can be extracted illustrating the feasibility of this approach.
104(1998); http://dx.doi.org/10.1121/1.423279View Description Hide Description
The principal aim of this work is to estimate, or to approximate, the complex -space spectrum of the wave field arriving on a linear array. First, using linear approximation, the location-dependent effect of the wave field magnitudes is modeled as an extra “loss” factor in the complex spectral variable. This complex spectrum model may provide a better description of the physical process and require less sensor elements than the real spectrum model because of the additional degree of freedom provided by the “loss” factor. A high-resolution algorithm combining the singular value decomposition method and the eigen-matrix pencil method is then employed to find the complex spectra representing the incoming real spectrum and the location dependent factors of multipath and multimode arrivals. Five key features (noise immunity, robustness, resolution, accuracy, and physical insight) of the proposed algorithm are studied using numerical examples.
Linking auto- and cross-correlation functions with correlation equations: Application to estimating the relative travel times and amplitudes of multipath104(1998); http://dx.doi.org/10.1121/1.423257View Description Hide Description
A location problem is considered where the sound which propagates along multipath are impractical to model because the environment is poorly known. The acoustic bandwidth is assumed to be large enough so that the cross-correlation functions between pairs of receivers contain multiple peaks from multipath. The highest peak may not correspond to the difference in path lengths between the source and the receivers. Using similarities in the patterns of peaks in auto- and cross-correlation functions, an algorithm is developed to identify which cross-correlation peak corresponds to the difference in first arrivals, which can be used for locating the source if these arrivals are straight. The similarities are expressed with new “correlation equations.” The number of lag-type correlation equations is , where is the typical number of multipath at each of receivers. The correlation equations may be impractical to solve exactly. Accurate solutions are found in simulations for the numbers, relative travel times, and amplitudes of all the multipath with the aid of a new “augmented-template correlation function” which is a cross-correlation of nonnegative lags of an auto-correlation function with lags from a cross-correlation function. The technique relies on time series which are filtered to yield one dominant source.
104(1998); http://dx.doi.org/10.1121/1.423291View Description Hide Description
A new approach is introduced for self-focusingphased arrays through inhomogeneous media for therapeutic and imaging applications. This algorithm utilizes solutions to the inverse scattering problem to estimate the impulse response (Green’s function) of the desired focal point(s) at the elements of the array. This approach is a two-stage procedure, where in the first stage the Green’s functions is estimated from measurements of the scattered field taken outside the region of interest. In the second stage, these estimates are used in the pseudoinverse method to compute excitation weights satisfying predefined set of constraints on the structure of the field at the focus points. These scalar, complex valued excitation weights are used to modulate the incident field for retransmission. The pseudoinverse pattern synthesis method requires knowing the Green’s function between the focus points and the array, which is difficult to attain for an unknown inhomogeneous medium. However, the solution to the inverse scattering problem, the scattering function, can be used directly to compute the required inhomogeneous Green’s function. This inverse scattering based self-focusing is noninvasive and does not require a strong point scatterer at or near the desired focus point. It simply requires measurements of the scattered field outside the region of interest. It can be used for high resolution imaging and enhanced therapeutic effects through inhomogeneous media without making any assumptions on the shape, size, or location of the inhomogeneity. This technique is outlined and numerical simulations are shown which validate this technique for single and multiple focusing using a circular array.